Overview
Spatial Data Science advances students from geographic information system operation to computational, statistical, and reproducible analysis of spatial data. The course develops workflows using programming languages, geospatial libraries, APIs, open data, version control, and reproducible notebooks, integrating tabular, vector, raster, and time-enabled datasets.
Students examine exploratory spatial data analysis, spatial autocorrelation and dependence, spatial heterogeneity, point-pattern analysis, interpolation, geostatistics, spatial regression, geographically weighted methods, network and location analysis, raster and remote-sensing models, and introductory machine learning for geographic prediction. Emphasis is placed on spatial scale, zoning, edge effects, coordinate systems, sampling design, uncertainty, and the modifiable areal unit problem.
Through practical exercises and an applied research project, students formulate spatial questions, construct reproducible analytical pipelines, select and justify appropriate methods, validate models, compare explanatory and predictive performance, communicate uncertainty, and assess limitations in studies of environmental, social, health, or urban problems.
Learning Outcomes
- Formulate researchable spatial questions and identify appropriate data structures, sources, sampling strategies, and analytical methods.
- Construct reproducible spatial data-science workflows using programming languages, geospatial libraries, APIs, version control, and computational notebooks.
- Integrate, clean, transform, and document tabular, vector, raster, and time-enabled spatial datasets using appropriate coordinate systems.
- Evaluate spatial scale, zoning, edge effects, spatial dependence, heterogeneity, sampling design, and the modifiable areal unit problem.
- Apply exploratory spatial analysis, point-pattern analysis, interpolation, geostatistical, network, raster, and spatial regression methods to geographic problems.
- Compare geographically weighted and machine-learning approaches with respect to assumptions, interpretability, predictive performance, and computational requirements.
- Validate spatial models and interpret analytical outputs, uncertainty, error, and limitations using appropriate diagnostic and performance measures.
- Synthesize and communicate a responsible spatial analysis through visualizations, technical documentation, and an evidence-based project report.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
| Workshop | 2 hours | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Weekly computational exercises (8 × 2.5%) | 20.00% |
| Test | Spatial analysis practical test | 15.00% |
| Deliverable | Reproducible workflow submission | 15.00% |
| Assignment | Research project proposal and data plan | 10.00% |
| Capstone | Research project report and presentation | 30.00% |
| Exam | Final examination | 10.00% |
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.